Machine Learning Engineer, Search ML- Mercari

JD in Japanese follows. 英文の後に和文JDをご覧いただけます。 Machine Learning Engineer, Search ML- Mercari Employment Status:　Full-time Work Hours: Full Flextime (no core time) Office: Roppongi For more details, see the Overview of Our Positions section on our Careers site. 　　 About Mercari Circulate all forms of value to unleash the potential in all people "What can I do to help society thrive with the finite resources we have?" The Mercari marketplace app was born in 2013 out of this thought by our founder Shintaro Yamada as he traveled the world. We believe that by circulating all forms of value, not just physical things and money, we can create opportunities for anyone to realize their dreams and contribute to society and the people around them. Mercari aims to use technology to connect people all over the world and create a world where anyone can unleash their potential. For more information about Mercari Group’s mission, see Mercari’s Culture Doc Organization/Team Mission Mercari Engineering Principles Mercari Engineering Principles are a shared understanding that serves as the foundation of engineering beliefs and behavior at Mercari. The Engineering Principles are designed to complement the organizational identity (Mercari’s mission, values, and culture) from an engineering viewpoint. These principles ultimately help us achieve Mercari’s mission by defining the ideal state we seek to realize in the long term. Passion For The Product Grow Together Solve Through Mechanisms Collaborate Openly For more details, please see the following link: Engineering Culture See here for more information about our mission and values. Work Responsibilities Elevate the Buyer Journey: Drive the optimization of item search and discovery capabilities at Mercari; take end-to-end ownership of core search business logic and user experience design. Architect Next-Gen AI Models: Design and deploy state-of-the-art machine learning models and robust training pipelines (incorporating Hybrid Search, Learning-to-Rank, Agentic Shopping, etc.) while continuously enhancing system reproducibility and debuggability. Standardize Experimentation Frameworks: Spearhead the development of standardized evaluation and experimentation infrastructure—including offline evaluation frameworks, online A/B testing guardrails, monitoring dashboards, and alerting systems—to accelerate iteration cycles and mitigate regressions. Drive Cross-Functional Alignment: Partner seamlessly with infrastructure, product, and adjacent engineering teams to align on scalability, latency, reliability, and cost efficiency, while orchestrating complex, cross-service architectural changes. Unique Challenges High-Velocity C2C Ecosystem: Scale and operate search systems within a highly dynamic, real-time C2C marketplace characterized by rapid inventory turnover (constant new listings, instant sell-outs, frequent price fluctuations) and persistent long-tail/cold-start constraints. UGC Semantic Bridging: Bridge the semantic gap between intent-driven user queries and highly fragmented, user-generated content (UGC) that features language-specific variations, slang, mixed scripts, typos, and unstructured brand/model references. Cutting-Edge AI Operationalization: Harness and integrate advanced deep learning architectures and Large Language Model (LLM) applications to redefine the modern e-commerce shopping experience. Qualifications Required Experience/Skills Experience: 5+ years of professional experience in Machine Learning or Search Engineering. Search Expertise: Proven track record of optimizing end-to-end search experiences using data-driven methodologies (e.g., hybrid retrieval, learning-to-rank, semantic search, and advanced evaluation metrics). Production Engineering: Hands-on experience with production-grade ML/Search serving infrastructure, including one or more of the following: online inference services, feature stores, embedding spaces, evaluation/experimentation tooling, or offline indexing pipelines. Research-to-Practice: Demonstrated experience continuously following academic research in your area of expertise (e.g., computer vision, NLP, recommender systems — CVPR, ICCV, ACL, EMNLP, SIGIR, KDD, RecSys, NeurIPS, ICML, etc.) and translating those insights into evaluated, implemented solutions that drove business impact. Applied Rigor: Ability to adapt academic techniques to production constraints (latency, scale, cost) and validate impact through methods such as A/B testing. Soft Skills: Strong collaborative and communication skills, with a demonstrated ability to align diverse technical teams and cross-functional stakeholders. Preferred Experience/Skills Backend Mastery: 5+ years of software engineering experience focusing on large-scale backend architectures. Scale & Performance: Deep technical expertise in large-scale search/recommendation backends, including distributed systems, high-concurrency/low-latency tuning, and complex system debugging. Academic Track Record: Publication or peer-review experience at top-tier conferences (e.g., CVPR, ICCV, ECCV, ACL, EMNLP, NAACL, SIGIR, KDD, WWW, RecSys, NeurIPS, ICML, etc.), or a PhD. Research Habit: A habit of reading several to ten papers a month to stay current with new methods. Technical Leadership: Experience independently driving the technical direction of a team or organization. Language Japanese: Nice to have (not required) English: Business level or above For details about CEFR, see here . Learn More About Mercari Group Careers site: https://careers.mercari.com/en/ Mercan: https://mercan.mercari.com/en/ Social media: X / Linkedin Recruiting at Mercari At Mercari Group, we value empathizing with and embodying the mission and values ​​of the Group and each company. To promote the creation of an organization that maximizes the total amount of value exhibited by all members, we would like to understand the experience and skills of each candidate as accurately as possible. Recruiting cycle at Mercari Group Application screening Skill assessment: For engineering positions, you will be asked to complete a skill assessment on HackerRank or GitHub. For non-engineering positions, you may be asked to complete an assessment depending on the position. (The timing of the assessment may coincide with the interview process.) Interview: The number of interviews may vary depending on the position. Reference check: We will ask for online references around the timing of the final interview. Offer: Offers will be determined carefully in consideration of the final interview and the reference check. 　Learn more about our recruiting process here . Equal Opportunity Hiring Here at Mercari, we work to realize a world in which no one’s potential is limited by their background and everyone has the opportunity to freely create value. We also firmly believe that a mindset of Inclusion & Diversity is essential for us to achieve our mission. This, of course, extends to our hiring practices as well. Mercari is committed to eliminating discrimination based on age, gender, sexual orientation, race, religion, physical disability, and other such factors so that anyone who shares our mission and values can join us, regardless of their background. For more details, please read our I&D statement . Please read and acknowledge our Privacy Policy prior to submitting your application. Machine Learning Engineer, Search ML- Mercari 雇用形態：　 正社員 働き方： フレックスタイム制（コアタイムなし・フレキシブルタイムなし） 勤務地： 六本木 詳細はキャリアサイトの 募集要項 よりご確認ください メルカリグループについて あらゆる価値を循環させ、あらゆる人の可能性を広げる 「地球資源が限られているなか、より豊かな社会をつくるために何ができるか」。2013年、創業者の山田進太郎が世界一周の旅で抱いた課題意識から、フリマアプリ「メルカリ」は生まれました。私たちは、物理的なモノやお金に限らずあらゆる価値を循環させることで、誰もがやりたいことを実現し、人や社会に貢献するための選択肢を増やすことができると信じています。 テクノロジーの力で世界中の人々をつなぎ、あらゆる人の可能性が発揮される世界を実現していきます。メルカリグループの目指すべき方針については Mercari Culture Doc をご覧ください。 組織・チームのミッション Mercari Engineering Principles Mercari Engineering Principles は、メルカリのエンジニアリング組織における信念や行動の基盤となる